加布里埃莱·法里纳 (Gabriele Farina) 在意大利北部丘陵酿酒区的一个小镇长大。他的父母都没有大学学位,，尽管他们都相信自己“不理解数学,” Farina 说, 他们给他买了他想要的技术书籍，并且’t 没有阻止他进入以科学为导向的, 而不是经典的, 高中。
Gabriele Farina grew up in a small town in a hilly winemaking region of northern Italy. Neither of his parents had college degrees, and although both were convinced they “didn’t understand math,” Farina says, they bought him the technical books he wanted and didn’t discourage him from attending the science-oriented, rather than the classical, high school.
到 14, 左右时，法里纳 (Farina) 就专注于一个对他的职业生涯至关重要的想法。
By around age 14, Farina had focused on an idea that would prove foundational to his career.
“I 很早就对机器可以比人类更好地做出预测或决策的想法着迷,” 他说。 “事实上，人造数学和算法可以创建,在某种意义上,优于其创造者,的系统，同时构建在简单的构建块上,一直是我敬畏的主要来源。”
“I was fascinated very early by the idea that a machine could make predictions or decisions so much better than humans,” he says. “The fact that human-made mathematics and algorithms could create systems that, in some sense, outperform their creators, all while building on simple building blocks, has always been a major source of awe for me.”
16, 时，Farina 编写了代码来解决他与 13 岁妹妹玩的棋盘游戏。
At age 16, Farina wrote code to solve a board game he played with his 13-year-old sister.
“I 使用一场又一场的比赛来计算最佳走法，并向我妹妹证明，早在我们亲眼目睹之前她就已经输了,” Farina 说, 补充说他的妹妹对他的新系统不太着迷。
“I used game after game to compute the optimal move and prove to my sister that she had already lost long before either of us could see it ourselves,” Farina says, adding that his sister was less enthralled with his new system.
就读于米兰理工大学, Farina 学习自动化与控制工程。随着时间的推移, 但是, 他意识到激发他兴趣的不是“ 只是应用已知技术, 而是理解和扩展他们的基础,” 他说。 “I 逐渐转向理论,，同时仍然非常关心演示该理论的具体应用。”
Enrolling at Politecnico di Milano for college, Farina studied automation and control engineering. Over time, however, he realized that what activated his interest was not “just applying known techniques, but understanding and extending their foundations,” he says. “I gradually shifted more and more toward theory, while still caring deeply about demonstrating concrete applications of that theory.”
Farina的 米兰理工大学顾问, Nicola Gatti, 计算机科学与工程教授兼研究员, 向 Farina 介绍了计算博弈论的研究问题，并鼓励他申请博士学位。当时, 是他直系亲属中第一个获得大学学位的人，住在意大利,，那里对博士学位的处理方式有所不同, Farina 说他 甚至不知道什么是博士学位。
Farina的 advisor at Politecnico di Milano, Nicola Gatti, professor and researcher in computer science and engineering, introduced Farina to research questions in computational game theory and encouraged him to apply for a PhD. At the time, being the first in his immediate family to earn a college degree and living in Italy, where doctoral degrees are handled differently, Farina says he didn’t even know what a PhD was.
尽管如此，, 本科毕业一个月后, Farina 开始在卡内基梅隆大学攻读计算机科学博士学位。在那里, 他的研究和论文, 获得了优异的成绩，并获得了 Facebook 经济学和计算奖学金。
Nevertheless, one month after graduating with his undergraduate degree, Farina began a doctoral degree in computer science at Carnegie Mellon University. There, he won distinctions for his research and dissertation, as well as a Facebook Fellowship in Economics and Computation.
在完成博士学位, Farina 时，他在 Meta的 基础人工智能研究实验室担任研究科学家一年。他的主要项目之一是帮助开发 Cicero,，一种能够在游戏中击败人类玩家的人工智能，该游戏涉及组建联盟, 谈判, 以及检测其他玩家何时虚张声势。
As he was finishing his doctorate, Farina worked for a year as a research scientist in Meta的 Fundamental AI Research Labs. One of his major projects was helping to develop Cicero, an AI that was able to beat human players in a game that involves forming alliances, negotiating, and detecting when other players are bluffing.
Farina 说, “当我们构建 Cicero, 时，我们对其进行了设计，如果不符合其利益，它就不会同意结成联盟,，并且它同样了解玩家是否可能撒谎,，因为他们按照自己的建议行事将违背自己的动机。”
Farina says, “when we built Cicero, we designed it so that it would not agree to form an alliance if it was not in its interest, and it likewise understood whether a player was likely lying, because for them to do as they proposed would be against their own incentives.”
《麻省理工学院技术评论》2022 年发表的一篇文章称，西塞罗可能代表了人工智能的进步，可以解决需要妥协的复杂问题。
A 2022 article in the MIT Technology Review said Cicero could represent advancement toward AIs that can solve complex problems requiring compromise.
在 Meta, 工作一年后，法里纳加入了麻省理工学院的教职。 2025,，他荣获国家科学基金会职业奖。他的工作—基于博弈论及其数学语言，描述了当不同各方有不同目标,时会发生什么，然后量化�%9均衡”，其中没有人有理由改变他们的策略—旨在简化大规模,复杂的现实世界场景，在这些场景中计算这样的均衡可能需要十亿年。
After his year at Meta, Farina joined the MIT faculty. In 2025, he was distinguished with the National Science Foundation CAREER Award. His work — based on game theory and its mathematical language describing what happens when different parties have different objectives, and then quantifying the “equilibrium” where no one has a reason to change their strategy — aims to simplify massive, complex real-world scenarios where calculating such an equilibrium could take a billion years.
“I 研究如何使用优化和算法来有效地实际找到这些稳定点,” 他说。 “我们的工作试图为理论的数学基础提供新的线索,更好地控制和预测这些复杂的动力系统,并使用这些想法来计算大型多智能体交互的良好解决方案。”
“I research how we can use optimization and algorithms to actually find these stable points efficiently,” he says. “Our work tries to shed new light on the mathematical underpinnings of the theory, better control and predict these complex dynamical systems, and uses these ideas to compute good solutions to large multi-agent interactions.”
Farina 对具有 “ 不完美信息,” 的设置特别感兴趣，这意味着某些代理拥有其他参与者未知的信息。在这种情况下，, 信息具有价值,，参与者必须对他们所拥有的信息采取战略性行动，以免泄露信息并降低其价值。扑克, 游戏中就有一个常见的例子，玩家通过虚张声势来隐藏自己的牌的信息。
Farina is especially interested in settings with “imperfect information,” which means that some agents have information that is unknown to other participants. In such scenarios, information has value, and participants must be strategic about acting on the information they possess so as not to reveal it and reduce its value. An everyday example occurs in the game of poker, where players bluff in order to conceal information about their cards.
根据 Farina, “，我们现在生活在一个机器比人类更擅长虚张声势的世界。”
According to Farina, “we now live in a world in which machines are far better at bluffing than humans.”
“大量不完美信息,”的情况让法里纳回到了棋盘游戏的起点。 Stratego 是一款军事策略游戏，它激发了花费数百万美元的研究工作来生产能够击败人类玩家的系统。需要复杂的风险计算和误导, 或虚张声势,，这可能是唯一一个付出巨大努力却未能产生超人表现的经典游戏, Farina 说。
A situation with “massive amounts of imperfect information,” has brought Farina back to his board-game beginnings. Stratego is a military strategy game that has inspired research efforts costing millions of dollars to produce systems capable of beating human players. Requiring complex risk calculation and misdirection, or bluffing, it was possibly the only classical game for which major efforts had failed to produce superhuman performance, Farina says.
凭借新的算法和训练成本低于$10,000,，而不是数百万,，Farina 和他的研究团队能够以 15 胜, 4 平, 1 负的成绩击败有史以来最好的球员—。 Farina 表示，他很高兴能如此经济地取得这样的结果,，他说，他希望“这些新技术将被纳入未来的管道,”。
With new algorithms and training costing less than $10,000, rather than millions, Farina and his research team were able to beat the best player of all time — with 15 wins, four draws, and one loss. Farina says he is thrilled to have produced such results so economically, and he hopes “these new techniques will be incorporated into future pipelines,” he says.
“我们已经看到在构建算法方面不断取得进展，这些算法可以在行动空间很大或信息不完善的情况下进行战略性推理并做出正确的决策。我很高兴看到这些算法融入到我们周围发生的的更广泛的人工智能革命中。”
“We have seen constant progress towards constructing algorithms that can reason strategically and make sound decisions despite large action spaces or imperfect information. I am excited about seeing these algorithms incorporated into the broader AI revolution that的 happening around us.”